#!/usr/bin/env python3 """ 🚀 智慧拓撲排序算法演示 這個演示展示了我們的進階pipeline拓撲分析和優化算法: - 自動依賴關係分析 - 循環檢測和解決 - 並行執行優化 - 關鍵路徑分析 - 性能指標計算 適合進度報告展示！ """ import json from mflow_converter import MFlowConverter def create_demo_pipeline() -> dict: """創建一個複雜的多階段pipeline用於演示""" return { "project_name": "Advanced Multi-Stage Fire Detection Pipeline", "description": "Demonstrates intelligent topology sorting with parallel stages", "nodes": [ # Input Node { "id": "input_001", "name": "RGB Camera Input", "type": "ExactInputNode", "pos": [100, 200], "properties": { "source_type": "Camera", "device_id": 0, "resolution": "1920x1080", "fps": 30 } }, # Parallel Feature Extraction Stages { "id": "model_rgb_001", "name": "RGB Feature Extractor", "type": "ExactModelNode", "pos": [300, 100], "properties": { "model_path": "rgb_features.nef", "scpu_fw_path": "fw_scpu.bin", "ncpu_fw_path": "fw_ncpu.bin", "dongle_series": "520", "port_id": "28,30" } }, { "id": "model_edge_002", "name": "Edge Feature Extractor", "type": "ExactModelNode", "pos": [300, 200], "properties": { "model_path": "edge_features.nef", "scpu_fw_path": "fw_scpu.bin", "ncpu_fw_path": "fw_ncpu.bin", "dongle_series": "520", "port_id": "32,34" } }, { "id": "model_thermal_003", "name": "Thermal Feature Extractor", "type": "ExactModelNode", "pos": [300, 300], "properties": { "model_path": "thermal_features.nef", "scpu_fw_path": "fw_scpu.bin", "ncpu_fw_path": "fw_ncpu.bin", "dongle_series": "520", "port_id": "36,38" } }, # Intermediate Processing Stages { "id": "model_fusion_004", "name": "Feature Fusion", "type": "ExactModelNode", "pos": [500, 150], "properties": { "model_path": "feature_fusion.nef", "scpu_fw_path": "fw_scpu.bin", "ncpu_fw_path": "fw_ncpu.bin", "dongle_series": "720", "port_id": "40,42" } }, { "id": "model_attention_005", "name": "Attention Mechanism", "type": "ExactModelNode", "pos": [500, 250], "properties": { "model_path": "attention.nef", "scpu_fw_path": "fw_scpu.bin", "ncpu_fw_path": "fw_ncpu.bin", "dongle_series": "720", "port_id": "44,46" } }, # Final Classification Stage { "id": "model_classifier_006", "name": "Fire Classifier", "type": "ExactModelNode", "pos": [700, 200], "properties": { "model_path": "fire_classifier.nef", "scpu_fw_path": "fw_scpu.bin", "ncpu_fw_path": "fw_ncpu.bin", "dongle_series": "720", "port_id": "48,50" } }, # Output Node { "id": "output_007", "name": "Detection Output", "type": "ExactOutputNode", "pos": [900, 200], "properties": { "output_type": "Stream", "format": "JSON", "destination": "tcp://localhost:5555" } } ], "connections": [ # Input to parallel feature extractors {"output_node": "input_001", "output_port": "output", "input_node": "model_rgb_001", "input_port": "input"}, {"output_node": "input_001", "output_port": "output", "input_node": "model_edge_002", "input_port": "input"}, {"output_node": "input_001", "output_port": "output", "input_node": "model_thermal_003", "input_port": "input"}, # Feature extractors to fusion {"output_node": "model_rgb_001", "output_port": "output", "input_node": "model_fusion_004", "input_port": "input"}, {"output_node": "model_edge_002", "output_port": "output", "input_node": "model_fusion_004", "input_port": "input"}, {"output_node": "model_thermal_003", "output_port": "output", "input_node": "model_attention_005", "input_port": "input"}, # Intermediate stages to classifier {"output_node": "model_fusion_004", "output_port": "output", "input_node": "model_classifier_006", "input_port": "input"}, {"output_node": "model_attention_005", "output_port": "output", "input_node": "model_classifier_006", "input_port": "input"}, # Classifier to output {"output_node": "model_classifier_006", "output_port": "output", "input_node": "output_007", "input_port": "input"} ], "version": "1.0" } def demo_simple_pipeline(): """演示簡單的線性pipeline""" print("🎯 DEMO 1: Simple Linear Pipeline") print("="*50) simple_pipeline = { "project_name": "Simple Linear Pipeline", "nodes": [ {"id": "model_001", "name": "Detection", "type": "ExactModelNode", "properties": {"model_path": "detect.nef", "scpu_fw_path": "fw_scpu.bin", "ncpu_fw_path": "fw_ncpu.bin", "port_id": "28"}}, {"id": "model_002", "name": "Classification", "type": "ExactModelNode", "properties": {"model_path": "classify.nef", "scpu_fw_path": "fw_scpu.bin", "ncpu_fw_path": "fw_ncpu.bin", "port_id": "30"}}, {"id": "model_003", "name": "Verification", "type": "ExactModelNode", "properties": {"model_path": "verify.nef", "scpu_fw_path": "fw_scpu.bin", "ncpu_fw_path": "fw_ncpu.bin", "port_id": "32"}} ], "connections": [ {"output_node": "model_001", "input_node": "model_002"}, {"output_node": "model_002", "input_node": "model_003"} ] } converter = MFlowConverter() config = converter._convert_mflow_to_config(simple_pipeline) print("\n") def demo_parallel_pipeline(): """演示並行pipeline""" print("🎯 DEMO 2: Parallel Processing Pipeline") print("="*50) parallel_pipeline = { "project_name": "Parallel Processing Pipeline", "nodes": [ {"id": "model_001", "name": "RGB Processor", "type": "ExactModelNode", "properties": {"model_path": "rgb.nef", "scpu_fw_path": "fw_scpu.bin", "ncpu_fw_path": "fw_ncpu.bin", "port_id": "28"}}, {"id": "model_002", "name": "IR Processor", "type": "ExactModelNode", "properties": {"model_path": "ir.nef", "scpu_fw_path": "fw_scpu.bin", "ncpu_fw_path": "fw_ncpu.bin", "port_id": "30"}}, {"id": "model_003", "name": "Depth Processor", "type": "ExactModelNode", "properties": {"model_path": "depth.nef", "scpu_fw_path": "fw_scpu.bin", "ncpu_fw_path": "fw_ncpu.bin", "port_id": "32"}}, {"id": "model_004", "name": "Fusion Engine", "type": "ExactModelNode", "properties": {"model_path": "fusion.nef", "scpu_fw_path": "fw_scpu.bin", "ncpu_fw_path": "fw_ncpu.bin", "port_id": "34"}} ], "connections": [ {"output_node": "model_001", "input_node": "model_004"}, {"output_node": "model_002", "input_node": "model_004"}, {"output_node": "model_003", "input_node": "model_004"} ] } converter = MFlowConverter() config = converter._convert_mflow_to_config(parallel_pipeline) print("\n") def demo_complex_pipeline(): """演示複雜的多層級pipeline""" print("🎯 DEMO 3: Complex Multi-Level Pipeline") print("="*50) complex_pipeline = create_demo_pipeline() converter = MFlowConverter() config = converter._convert_mflow_to_config(complex_pipeline) # 顯示額外的配置信息 print("🔧 Generated Pipeline Configuration:") print(f" • Stage Configs: {len(config.stage_configs)}") print(f" • Input Config: {config.input_config.get('source_type', 'Unknown')}") print(f" • Output Config: {config.output_config.get('format', 'Unknown')}") print("\n") def demo_cycle_detection(): """演示循環檢測和解決""" print("🎯 DEMO 4: Cycle Detection & Resolution") print("="*50) # 創建一個有循環的pipeline cycle_pipeline = { "project_name": "Pipeline with Cycles (Testing)", "nodes": [ {"id": "model_A", "name": "Model A", "type": "ExactModelNode", "properties": {"model_path": "a.nef", "scpu_fw_path": "fw_scpu.bin", "ncpu_fw_path": "fw_ncpu.bin", "port_id": "28"}}, {"id": "model_B", "name": "Model B", "type": "ExactModelNode", "properties": {"model_path": "b.nef", "scpu_fw_path": "fw_scpu.bin", "ncpu_fw_path": "fw_ncpu.bin", "port_id": "30"}}, {"id": "model_C", "name": "Model C", "type": "ExactModelNode", "properties": {"model_path": "c.nef", "scpu_fw_path": "fw_scpu.bin", "ncpu_fw_path": "fw_ncpu.bin", "port_id": "32"}} ], "connections": [ {"output_node": "model_A", "input_node": "model_B"}, {"output_node": "model_B", "input_node": "model_C"}, {"output_node": "model_C", "input_node": "model_A"} # Creates cycle! ] } converter = MFlowConverter() config = converter._convert_mflow_to_config(cycle_pipeline) print("\n") def demo_performance_analysis(): """演示性能分析功能""" print("🎯 DEMO 5: Performance Analysis") print("="*50) # 使用之前創建的複雜pipeline complex_pipeline = create_demo_pipeline() converter = MFlowConverter() config = converter._convert_mflow_to_config(complex_pipeline) # 驗證配置 is_valid, errors = converter.validate_config(config) print("🔍 Configuration Validation:") if is_valid: print(" ✅ All configurations are valid!") else: print(" ⚠️ Configuration issues found:") for error in errors[:3]: # Show first 3 errors print(f" - {error}") print(f"\n📦 Ready for InferencePipeline Creation:") print(f" • Total Stages: {len(config.stage_configs)}") print(f" • Pipeline Name: {config.pipeline_name}") print(f" • Preprocessing Configs: {len(config.preprocessing_configs)}") print(f" • Postprocessing Configs: {len(config.postprocessing_configs)}") print("\n") def main(): """主演示函數""" print("🚀 INTELLIGENT PIPELINE TOPOLOGY SORTING DEMONSTRATION") print("="*60) print("This demo showcases our advanced pipeline analysis capabilities:") print("• Automatic dependency resolution") print("• Parallel execution optimization") print("• Cycle detection and prevention") print("• Critical path analysis") print("• Performance metrics calculation") print("="*60 + "\n") try: # 運行所有演示 demo_simple_pipeline() demo_parallel_pipeline() demo_complex_pipeline() demo_cycle_detection() demo_performance_analysis() print("🎉 ALL DEMONSTRATIONS COMPLETED SUCCESSFULLY!") print("Ready for production deployment and progress reporting! 🚀") except Exception as e: print(f"❌ Demo error: {e}") import traceback traceback.print_exc() if __name__ == "__main__": main()